Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning

TLDR

Adaptive fuzzy neural network control is needed for constrained robots operating under unknown dynamics, state constraints, and uncertain compliant environments, where prior knowledge of uncertainty is unavailable. The study aims to develop a fuzzy neural network learning algorithm that identifies the uncertain plant model and enables adaptive impedance control for constrained robots. The authors design a fuzzy neural network that learns the plant dynamics, incorporate impedance learning to guide the robot toward desired trajectories, employ a barrier Lyapunov function to enforce state constraints, and validate the approach with simulation studies. The proposed control guarantees Lyapunov stability and reliable tracking performance despite state constraints and uncertainty.

Abstract

This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there is no need to get the prior knowledge about the uncertainty and a sufficient amount of observed data. Also, impedance learning is introduced to tackle the interaction between the robot and its environment, so that the robot follows a desired destination generated by impedance learning. A barrier Lyapunov function is used to address the effect of state constraints. With the proposed control, the stability of the closed-loop system is achieved via Lyapunov's stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty. Some simulation studies are carried out to illustrate the effectiveness of the proposed scheme.